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arXiv 2103.12274gr-qcastro-ph.HEastro-ph.IM
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第三代天文台双星中子星合并产生的引力波的贝叶斯推断

Bayesian inference for gravitational waves from binary neutron star mergers in third-generation observatories

Rory Smith, Ssohrab Borhanian, Bangalore Sathyaprakash, Francisco Hernandez Vivanco, Scott Field, Paul Lasky, Ilya Mandel, Soichiro Morisaki, David Ottaway, Bra… 展开作者

Rory Smith, Ssohrab Borhanian, Bangalore Sathyaprakash, Francisco Hernandez Vivanco, Scott Field, Paul Lasky, Ilya Mandel, Soichiro Morisaki, David Ottaway, Bram Slagmolen, Eric Thrane, Daniel Töyrä, Salvatore Vitale

摘要:

第三代(3G)引力波探测器将以前所未有的精确度观测数千颗合并的中子星双星。由于高信噪比和长持续时间的信号,从这些信号中提取最高精度的科学预计是具有挑战性的。我们证明,目前的贝叶斯推断范例可以扩展到二进制中子星信号的分析,而不会打破计算银行。我们构建了降阶模型的$\sim 90\,\mathrm{minute}$长引力波信号,覆盖观测波段($5-2048\,\mathrm{Hz}$),加快了推理的一个因素相比,没有降阶模型的计算时间$\sim 1.3\times 10^4$。降阶模型包括关键的物理,包括潮汐变形,振幅调制由于地球的旋转,和自旋引起的轨道进动的影响。我们展示了降阶建模如何加速对包含多个重叠引力波信号的数据的推断,并将加速确定为重叠信号数量的函数。因此,我们得出结论,贝叶斯推断是计算上易于处理的长寿,重叠,高信号噪声比的事件存在于3G观测站。

英文摘要:

Third-generation (3G) gravitational-wave detectors will observe thousands of coalescing neutron star binaries with unprecedented fidelity. Extracting the highest precision science from these signals is expected to be challenging owing to both high signal-to-noise ratios and long-duration signals. We demonstrate that current Bayesian inference paradigms can be extended to the analysis of binary neutron star signals without breaking the computational bank. We construct reduced order models for $\sim 90\,\mathrm{minute}$ long gravitational-wave signals, covering the observing band ($5-2048\,\mathrm{Hz}$), speeding up inference by a factor of $\sim 1.3\times 10^4$ compared to the calculation times without reduced order models. The reduced order models incorporate key physics including the effects of tidal deformability, amplitude modulation due to the Earth's rotation, and spin-induced orbital precession. We show how reduced order modeling can accelerate inference on data containing multiple, overlapping gravitational-wave signals, and determine the speedup as a function of the number of overlapping signals. Thus, we conclude that Bayesian inference is computationally tractable for the long-lived, overlapping, high signal-to-noise-ratio events present in 3G observatories.

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